Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
arXiv:2511.05017v3 Announce Type: replace-cross Abstract: Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual information during multimodal reasoning. A key cause is the model's over-reliance on textual priors and underutilization of visual cues, leading to outputs that are linguistically fluent but visually inaccurate. For example, given an image of an empty kitchen countertop, an LVLM might hallucinate a "bowl of fruit" or "cup of coffee", relying on language associations rather than visual evidence. Most
The rapid advancement and widespread deployment of Large Vision-Language Models make addressing their 'hallucination' problem a critical and timely research focus, particularly as these models move towards more autonomous applications.
Mitigating hallucinations is crucial for the reliability and trustworthiness of AI systems deployed in real-world scenarios, impacting everything from content generation to decision support where factual accuracy is paramount.
Improved methods for integrating visual information into LVLMs will lead to more factually grounded and less prone-to-hallucination multimodal AI, enhancing their utility and safety.
- · AI developers
- · Enterprise AI adopters
- · Vision-Language Model researchers
- · Applications relying on unmitigated hallucinating LVLMs
- · Developers neglecting multimodal alignment
More reliable and accurate outputs from multimodal AI models will emerge.
This reliability will accelerate the adoption of LVLMs in sensitive applications requiring high fidelity to visual data.
Increased trust in LVLMs could lead to their integration into autonomous AI agents, blurring the lines between digital and physical perception.
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